While reading Erick Schonfeld’s post about how It’s Time to Hide The Noise, in reference to how twitter clients still fail to float interesting tweets to the top, I started thinking about some features that would help get this process started.

Since I’m too busy (ok, lazy) to build any of these myself, I figured I’d just write about some features that I would definitely use and that would be more than enough for me to switch twitter clients (again). I use brizzly right now (switched from Tweetdeck), which does a great job, but still doesn’t do much to help filter the noise.

Smarter Categories:

Most clients let you categorize the people you follow, including Twitter itself with their forthcoming “lists” feature. This is somewhat useful but the problem is that the people I follow tend to have incredibly insightful tweets amidst a sea of inane ones. Right when I’m about to unfollow someone after stumbling through their stream of drivel, BOOM they send out a link to an awesome article or blog post that I otherwise wouldn’t have found. I’ve learned so many great things from the people I follow that it justifies wasting the time it takes to get through the notes about what they’re eating, when their “wheels are down” and other empty nuggets of the sort.

This wouldn’t be easy, but I think it would be doable: why not let us categorize tweets? Build your own list of categories or select from a pre-set list. Then, as you use the client and come across a tweet worth-while tweet, categorize it as such. The engine would then analyze the following characteristics about the message:

Who it came from

What it linked to (follow the shortener link to see which news source or blog it referenced)

What kind of media was in the link? (Image, video, article, flash, whatever)

Is it being retweeted? If so, are other people I follow retweeting (that may imply it is more relevant to me)?

Are other twitter users I follow mentioned in the tweet?

The engine could then learn that I tend to think articles on TechCrunch, SFIst, LifeHacker, Consumerist, NYT, WSJ, etc are useful and thus should be somehow conveyed to me as more important. It will also identify the users that tend to send more interesting tweets.

Lexicon Analysis:

This feature would definitely prove to have some false positives (and vice-versa) but would nonetheless be worthwhile to try out and improve with time. Sort of like a spam filter, the user could create a list of words or phrases that would be used to filter the stream. Mine would surely include:

“flight to”

“wheels down”

“lunch”

“eating”

“Don’t Like”:

Like facebook’s “like” button, a great twitter client would have a “don’t like” button that could be pressed for any given tweet in the stream. The engine would start to then find patterns in the tweets you don’t like which would then suggest or at least help you build rules that could be set up to better manage the stream.

My twitter stream gets almost as much attention as my email stream does and is just about my most important news source. I think twitter clients have a long way to go before managing twitter is as efficient as managing email, and these features would help get us there.

I’ve been doing a lot of A/B testing on the landing page of a side project website I have called VIN-History.com. The goal of the site is to capture organic searches for VIN numbers (the unique, 17-character serial number that every production automobile in the world has). People put VIN numbers in Google to try to find more information about that car. I try to give it to them (to the extent my database has any) and then help them find out the market value of the car, its maintenence schedule and how to buy a full vehicle history report from Experian’s AutoCheck.com.

How to test (plus some actual code):There are a lot of ways to do this, but I’ve found that what makes the most sense is to give each unique session either version A or B for that session only. If they close the browser and come back at a later date, they have a 50/50 chance of getting A or B. If they hit the page you’re testing 10 times in the same session, they’ll always see either A or B, whichever they were assigned when they arrived. This keeps things consistent and doesn’t distract or confuse the user.

Steps To Implement:

In the main page that will call either verison A or B of the content, assign this visitor either “A” or “B” for the rest of the session. First see if there is already a session cookie set. If so, get the value. If not, set one.

$ab is the variable that holds whether the user is going to see version A or B. Now, wherever you want to include the code you’re testing, you do a simple line that includes the appropriate file. It helps if you can name the include file with an “A” or “B” in it to keep things simple.

Be sure you include Google Analytics tracking codes on the different actions you want to measure. On my landing page, a user can do 1 of 3 things: Get an AutoCheck report, find out the market value of the car, view the service schedule for the car. All 3 are links or form POSTs that take the user away from the site. Each action is worth something different to me and I’d like to measure which page layout yields better results for each action and overall less dropoff from the page (bounce rate).
– The text ‘/ds-psr-a’ is completely arbitrary. It doesn’t even have to be a valid URL. It is just a unique string that will show up in the Google Analytics report later as an action that was taken by a user. For this particular action, I used /ds-psr-a and /ds-psr-b to track the same action on two different versions of the page.

The First Test

The goal is to test goal conversion on the VIN number landing page. I’ll use this VIN as an example: 1N6AA07B55N529895
Here are the two versions of the landing page I decided to test first. A is the original, B has some significant changes. Both have the same 3 actions a user can choose from.

The Results:

After testing both versions for about 2 weeks, the results from Google Analytics were pretty conclusive:

Goal 1: AutoCheck Report:
Version A sends almost 75% more traffic to AutoCheck than Version B does. Interestingly, sales through AutoCheck remained constant. This means that the leads being sent by Version B were more qualified and were converting much more efficiently. Net-net: Version A wastes traffic by sending too many users to Experian when that may not be what they’re really looking for. Pure gold!

Goal 2: Market Value Lookup: Versions A and B basically tied this race for the trial period. This is not surprising, considering that on both versions of the page, the Value Goal is pretty much the “second” thing on the menu. No action to be taken here.

Goal 3: Servicing Link:
Version B clearly converted on this goal better than Version A – 253% better. A lot of these clicks were probably ones that would have been wasted on Goal 1.

Round 1 Conclusion: So, it’s clear that Version B wins here by converting on 2 of the 3 goals more efficiently. Next step will be to come up with a new version B that can test a few more theories about converting even better. That will be in the next post … stay tuned.

Q: How has the global financial crisis affected the VC climate in China?
A: Exactly the same as the US. Not going to bother paraphrasing.

Q: What makes a Chinese Entrepreneur better / different?
A: US: decks are well structured, CEOs are polished and sharp. CHINA: much younger, more grass-roots entrepreneurs.
US: ready, aim, fire CHINA: ready, fire – go see what you killed US: still in the bunker figuring out what to shoot

Q: Advice to / opinions of expat entrepreneurs?
A: Tend to only invest in experienced Chinese entrepreneurs.
Understanding of Chinese culture (to know your users) is a requirement
More attractive: Chinese who leave, learn in US, come back
IP + good approach to business model coming to China from another country

Q: Post-Series-A, what does the cap structure look like between Founders / VC / employees?
A: Chinese founders will typically own more than in US. This is because of Chinese culture. Talent/engineering cost is very low. Angel round will get a company to 20-30 employees. Also, companies need less cash so founders end up keeping more. (lower cap-ex)

Q: What are the exit strategies here in China and what are the corresponding legal hurdles?
A: Shooting for overseas IPO or M&A. Company will be owned by a holding company based off-shore.
China working on a NASDAQ competitor starting Q1 2010 (Jimbot (sp?))
Trading volume in China is already 4x Hong Kong’s
PE ratios higher in China as well. All of this should help more local IPOs
Chinese consumers understand the concept of investing, also helpful

Q: How are Chinese entrepreneurs connecting with VCs (is there an angel/incubator equivalent in China?)
A: Events & conferences, lots of demos in China. Not many incubators yet.
There are some angel groups in Shanghai – not making a lot of progress yet. 60 angels ~ 4 deals. Each angel is committed to invest $5k/year = lots of startups.

Notes:

1-5mm fetching about 10-40% of a company normally here. (Softbank – backed by Cisco)

Funds are preserving so they can do follow-on investments for their portfolio companies

15 person company buring 12k/mo in China – 2nd largest language learning company in the world

in RMB investments there is no concept of preferred / common stock

Must have an RMB fund on-shore to invest in China

There are constraints on equity ownership by foreign investors. Forces investors to localize

There are still unclear tax issues after liquidity events in China

It’s about investing in people, just like in the US

Retaining people is key – much harded to do in China – competing with the big, stable companies. An investment in a charismatic, likeable leader carries a lot of weight

“You’re ATM card is less secure than your online gaming account in China” – the gaming acct has a lot more value built in

Service-based innovation in the US tends to be based on core technology innovation in China. Example: Kindle developed in China, commercialized in US.

Orange: in China to find business model innovations to bring back to Europe.

Behavioral targeting on the Internet: US you get in trouble (Beacon?) – in China it’s coming out of Government / University labs.
– Chinese gov’t is motivated in tracking people and their habits whereas US gov’t is not.